The ability for an agent to localize itself within an environment is crucial for many real-world applications. For unknown environments, Simultaneous Localization and Mapping (SLAM) enables incremental and concurrent building of and localizing within a map. We present a new, differentiable architecture, Neural Graph Optimizer, progressing towards a complete neural network solution for SLAM by designing a system composed of a local pose estimation model, a novel pose selection module, and a novel graph optimization process. The entire architecture is trained in an end-to-end fashion, enabling the network to automatically learn domain-specific features relevant to the visual odometry and avoid the involved process of feature engineering. We demonstrate the effectiveness of our system on a simulated 2D maze and the 3D ViZ-Doom environment.

Slides

Location

Orientation

Velocity

IR context -> Sociocultural context

Writing Fika. Make a few printouts of the abstract

It kinda happened. W

Write up LMN4A2P thoughts. Took the following and put them in a LMN4A2P roadmap document in Google Docs

Storing a corpora (raw text, BoW, TF-IDF, Matrix)

Uploading from file

Uploading from link/crawl

Corpora labeling and exploring

Index with ElasticSearch

Production of word vectors or ‘effigy documents’

Effigy search using Google CSE for public documents that are similar

General

Site-specific

Semantic (Academic, etc)

Search page

Lists (reweightable) or terms and documents

Cluster-based map (pan/zoom/search)

I’m as enthusiastic about the future of AI as (almost) anyone, but I would estimate I’ve created 1000X more value from careful manual analysis of a few high quality data sets than I have from all the fancy ML models I’ve trained combined. (Thread by Sean Taylor on Twitter, 8:33 Feb 19, 2018)

Prophet is a procedure for forecasting time series data. It is based on an additive model where non-linear trends are fit with yearly and weekly seasonality, plus holidays. It works best with daily periodicity data with at least one year of historical data. Prophet is robust to missing data, shifts in the trend, and large outliers.